Upregulation of α-synuclein during localized radiation therapy signals the association of cancer-related fatigue with the activation of inflammatory and neuroprotective pathways.

PURPOSE: Neuroinflammatory mechanisms are associated with fatigue in neurodegenerative conditions such as Parkinson's. The symptoms in Parkinson's including fatigue are thought to be related to α-synuclein overexpression. This study investigated genomic correlates of fatigue experienced by men with prostate cancer receiving external beam radiation therapy (EBRT). PATIENTS AND METHODS: Sixteen men with non-metastatic prostate cancer who were scheduled to receive EBRT were enrolled. Fatigue scores and blood were obtained at baseline (prior to EBRT, D0); one hour following initiation of EBRT (D1), day 7 (D7), day 14 (D14), midpoint (days 19-21, D21), completion (days 38-42, D42), and four weeks post-EBRT (days 68-72, D72). Gene expression profiling using microarray analysis was performed from peripheral blood and confirmatory qPCR and protein (ELISA) analyses verified the microarray results. Correlations between fatigue and gene/protein expressions were determined using a mixed model approach. RESULTS: Microarray data showed significant, differential expression of 463 probesets following EBRT. SNCA had a 2.95-fold change at D21 from baseline. SNCA expression was confirmed by qPCR (p<0.001) and ELISA (p<0.001) over time during EBRT. Fatigue scores were significantly correlated with SNCA gene expression on D14 (r=0.55, p<0.05) and plasma α-synuclein concentrations on D42 of EBRT (r=0.54, p=0.04). CONCLUSION: Fatigue experienced during EBRT may be mediated by α-synuclein overexpression. Alpha-synuclein may serve as a useful biomarker to understand the mechanisms and pathways related to the development of fatigue in this population.

Modeling the inhibitory effects of organic acids on bacteria.

The inhibitory effect of acids on microbial growth has long been used to preserve foods from spoilage. While much of the effect can be accounted for by pH, it is well known that different organic acids vary considerably in their inhibitory effects. Because organic acids are not members of a homologous series, but vary in the numbers of carboxy groups, hydroxy groups and carbon-carbon double bonds in the molecule, it has typically not been possible to predict the magnitude, or in some cases even the direction, of the change in inhibitory effect upon substituting one acid for another or to predict the net result in food systems containing more than one acid. The objective of this investigation was to attempt to construct a mathematical model that would enable such prediction as a function of the physical and chemical properties of organic acids. Principal Components Analysis (PCA) was applied to 11 properties for each of 17 acids commonly found in food systems; this resulted in four significant principal components (PCs), presumably representing fundamental properties of the acids and indicating each acid's location along each of these four scales. These properties correspond to polar groups, the number of double bonds, molecular size, and solubility in non-polar solvents. Minimum inhibitory concentrations (MICs) for each of eight acids for six test microorganisms were determined at pH 5.25. The MICs for each organism were modeled as a function of the four PCs using partial least squares (PLS) regression. This produced models with high correlations for five of the bacteria (R2 = 0.856, 0.941, 0.968, 0.968 and 0.970) and one with a slightly lower value (R2 = 0.785). Acid susceptible organisms (Bacillus cereus, Bacillus subtilis, and Alicyclobacillus) exhibited a similar response pattern. There appeared to be two separate response patterns for acid resistant organisms; one was exhibited by the two lactobacilli studied and the other by E. coli. Predicting the inhibitory effects of the organic acids as a function of their chemical and physical properties is clearly possible.